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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Formation of a Secondary Phase in Thermally Evaporated MAPbI 3 and Its Effects on Solar Cell Performance

Thermal evaporation is a promising deposition technique to scale up perovskite solar cells (PSCs) to large areas, but the lack of understanding of the mechanisms that lead to high-quality evaporated methylammonium lead triiodide (MAPbI 3 ) films gives rise to devices with efficiencies lower than those obtained by spin coating. Here this work investigates the crystalline properties of MAPbI 3 deposited by the thermal coevaporation of PbI 2 and MAI, where the MAI evaporation rate is controlled by setting different temperatures for the MAI source and the PbI 2 deposition rate is controlled with a quartz crystal microbalance (QCM). Using grazing incident wide-angle X-ray scattering (GIWAXS) and X-ray diffraction (XRD), we identify the formation of a secondary orthorhombic phase (with a Pnma space group) that appears at MAI source temperatures below 155 °C. With synchrotron-based X-ray fluorescence (XRF) microscopy, we show that the changes in crystalline phases are not necessarily due to changes in stoichiometry. The films show a stochiometric composition when the MAI source is heated between 140 to 155 °C, and the samples become slightly MAI rich at 165 °C. Increasing the MAI temperature beyond 165 °C introduces an excess of MAI in the film, which promotes the formation of films with low crystallinity that contain low-dimensional perovskites. When they are incorporated in solar cells, the films deposited at 165 °C result in the champion power conversion efficiency, although the presence of a small amount of low-dimensional perovskite may lead to a lower open-circuit voltage. We hypothesize that the formation of secondary phases in evaporated films limits the performance of PSCs and that their formation can be suppressed by controlling the MAI source temperature, bringing the film toward a phase-pure tetragonal structure. Control of the phases during perovskite evaporation is therefore crucial to obtain high-performance solar cells.

36 MATERIALS SCIENCE↗
DOE OSTI2025

powersqueeze

powersqueeze (psqz) is a truncated power iteration library intended for high-performance computing platforms. psqz efficiently produces low-dimensional, linear measurements of graph matrix spectra by combining classical power iteration with sparse Johnson-Lindenstrauss transforms. psqz is intended to produce high-quality, fast, data-oblivious low-dimensional representations of high-dimensional sparse data such as graphs and term-document matrices. psqz is intended to replace similar workflows that depend on directly approximating a truncated eigendecomposition (e.g., the first step of spectral clustering), which is a much more expensive operation.

Priest, BenjaminW [Lawrence Livermore National Lab↗
DOE OSTI2025

In Situ Data Analysis Through Physics-informed Tensor Decompositions (LDRD Final Report)

We introduce a new low-dimensional model of high-dimensional numerical simulation data based on low-rank tensor decompositions. Our new model aims to minimize differences between the model data and simulation data as well as functions of the model data and functions of the simulation data. This novel approach to dimensionality reduction of simulation data provides a means of directly incorporating quantities of interests and invariants associated with conservation principles associated with the simulation data into the low-dimensional model, thus enabling more accurate analysis of the simulation without requiring access to the full set of high-dimensional data. Computational results of applying this approach to two standard low-rank tensor decompositions of data arising from simulation of combustion and plasma physics are presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Photometric redshifts from SDSS images with an interpretable deep capsule network

ABSTRACT Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time are all critically dependent on estimates of photometric redshifts. Capsule networks are a new type of neural network architecture that is better suited for identifying morphological features of the input images than traditional convolutional neural networks. We use a deep capsule network trained on ugriz images, spectroscopic redshifts, and Galaxy Zoo spiral/elliptical classifications of ∼400 000 Sloan Digital Sky Survey galaxies to do photometric redshift estimation. We achieve a photometric redshift prediction accuracy and a fraction of catastrophic outliers that are comparable to or better than current methods for SDSS main galaxy sample-like data sets (r ≤ 17.8 and zspec ≤ 0.4) while requiring less data and fewer trainable parameters. Furthermore, the decision-making of our capsule network is much more easily interpretable as capsules act as a low-dimensional encoding of the image. When the capsules are projected on a two-dimensional manifold, they form a single redshift sequence with the fraction of spirals in a region exhibiting a gradient roughly perpendicular to the redshift sequence. We perturb encodings of real galaxy images in this low-dimensional space to create synthetic galaxy images that demonstrate the image properties (e.g. size, orientation, and surface brightness) encoded by each dimension. We also measure correlations between galaxy properties (e.g. magnitudes, colours, and stellar mass) and each capsule dimension. We publicly release our code, estimated redshifts, and additional catalogues at https://biprateep.github.io/encapZulate-1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Thickness-dependent polaron crossover in tellurene

Polarons, quasiparticles from electron-phonon coupling, are crucial for material properties including high-temperature superconductivity and colossal magnetoresistance. However, scarce studies have investigated polaron formation in low-dimensional materials with phonon polarity and electronic structure transitions. In this work, we studied polarons of tellurene, composed of chiral Te chains. The frequency and linewidth of the A 1 phonon, which becomes increasingly polar for thinner tellurene, change abruptly for thickness below 10 nanometers, where field-effect mobility drops rapidly. These phonon and transport signatures, combined with phonon polarity and band structure, suggest a crossover from large polarons in bulk tellurium to small polarons in few-layer tellurene. Effective field theory considering phonon renormalization in the small-polaron regime semiquantitatively reproduces the phonon hardening and broadening effects. This polaron crossover stems from the quasi–one-dimensional nature of tellurene, where modulation of interchain distance reduces dielectric screening and promotes electron-phonon coupling. Our work provides valuable insights into the influence of polarons on phononic, electronic, and structural properties in low-dimensional materials.

42 ENGINEERING↗
NASA NTRS2010

Algorithm for Stabilizing a POD-Based Dynamical System

This algorithm provides a new way to improve the accuracy and asymptotic behavior of a low-dimensional system based on the proper orthogonal decomposition (POD). Given a data set representing the evolution of a system of partial differential equations (PDEs), such as the Navier-Stokes equations for incompressible flow, one may obtain a low-dimensional model in the form of ordinary differential equations (ODEs) that should model the dynamics of the flow. Temporal sampling of the direct numerical simulation of the PDEs produces a spatial time series. The POD extracts the temporal and spatial eigenfunctions of this data set. Truncated to retain only the most energetic modes followed by Galerkin projection of these modes onto the PDEs obtains a dynamical system of ordinary differential equations for the time-dependent behavior of the flow. In practice, the steps leading to this system of ODEs entail numerically computing first-order derivatives of the mean data field and the eigenfunctions, and the computation of many inner products. This is far from a perfect process, and often results in the lack of long-term stability of the system and incorrect asymptotic behavior of the model. This algorithm describes a new stabilization method that utilizes the temporal eigenfunctions to derive correction terms for the coefficients of the dynamical system to significantly reduce these errors.

Kalb, Virginia L.↗
DOE OSTI2026

Deep nonparametric estimation of operators between infinite dimensional spaces

Learning operators between infinitely dimensional spaces is an important learning task arising in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class. Under the assumption that the target operator exhibits a low dimensional structure, our error bounds decay as the training sample size increases, with an attractive fast rate depending on the intrinsic dimension in our estimation. Our assumptions cover most scenarios in real applications and our results give rise to fast rates by exploiting low dimensional structures of data in operator estimation. We also investigate the influence of network structures (e.g., network width, depth, and sparsity) on the generalization error of the neural network estimator and propose a general suggestion on the choice of network structures to maximize the learning efficiency quantitatively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces

Learning operators between infinitely dimensional spaces is an important learning task arising in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class. Under the assumption that the target operator exhibits a low dimensional structure, our error bounds decay as the training sample size increases, with an attractive fast rate depending on the intrinsic dimension in our estimation. Our assumptions cover most scenarios in real applications and our results give rise to fast rates by exploiting low dimensional structures of data in operator estimation. We also investigate the influence of network structures (e.g., network width, depth, and sparsity) on the generalization error of the neural network estimator and propose a general suggestion on the choice of network structures to maximize the learning efficiency quantitatively.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

A search for a dynamical attractor in Cygnus X-1

A method used for searching for low-dimensional attractors in fluctuating signals is applied to the aperiodicity observed in Cyg X-1. The analysis technique for determining the dimension of an attractor given its light curve, and the practical limitations in applying the technique, are described. The requirement of large amounts of low-noise data is found to be very important in using the technique. No low-dimensional attractor is found for the set of Cyg X-1 data used, but evidence of an attractor with large dimension may be present.

Lochner, James C.↗
NASA NTRS1996

Nonlinear dynamics of the magnetosphere and space weather

The solar wind-magnetosphere system exhibits coherence on the global scale and such behavior can arise from nonlinearity on the dynamics. The observational time series data were used together with phase space reconstruction techniques to analyze the magnetospheric dynamics. Analysis of the solar wind, auroral electrojet and Dst indices showed low dimensionality of the dynamics and accurate prediction can be made with an input/output model. The predictability of the magnetosphere in spite of the apparent complexity arises from its dynamical synchronism with the solar wind. The electrodynamic coupling between different regions of the magnetosphere yields its coherent, low dimensional behavior. The data from multiple satellites and ground stations can be used to develop a spatio-temporal model that identifies the coupling between different regions. These nonlinear dynamical models provide space weather forecasting capabilities.

Sharma, A. Surjalal↗
DOE OSTI2023

Reduced-order autodifferentiable ensemble Kalman filters

This paper introduces a computational framework to reconstruct and forecast a partially observed state that evolves according to an unknown or expensive-to-simulate dynamical system. Our reduced-order autodifferentiable ensemble Kalman filters (ROAD-EnKFs) learn a latent low-dimensional surrogate model for the dynamics and a decoder that maps from the latent space to the state space. The learned dynamics and decoder are then used within an EnKF to reconstruct and forecast the state. Numerical experiments show that if the state dynamics exhibit a hidden low-dimensional structure, ROAD-EnKFs achieve higher accuracy at lower computational cost compared to existing methods. If such structure is not expressed in the latent state dynamics, ROAD-EnKFs achieve similar accuracy at lower cost, making them a promising approach for surrogate state reconstruction and forecasting.

Mathematics↗
NASA NTRS1991

Is there a strange attractor in the magnetosphere?

This paper reviews recent attempts to determine if some aspects of magnetospheric dynamics, and in particular substorms as measured by AE and AL geomagnetic indices, can be represented by a low-dimensional dynamical system. If true, this result would imply that a small set of ordinary differential equations could describe important aspects of substorm dynamics, greatly simplifying modeling and prediction efforts and providing significant input to more detailed modeling. The "embedding" and "correlation dimension" methods used to investigate the dimensionality of a physical process from a single time series are considered in detail with an emphasis on what can go wrong and what can be done about it. Two main conclusions of this work, which includes some new results on the particular case of AL, are (1) that a low-dimensional and probably strange attractor does exist in magnetospheric dynamics, and (2) that there is no reliable substitute for using large numbers of data points in performing analyses leading to this conclusion.

Roberts, D. A.↗
DOE OSTI2024

Exciton Transport in Perovskite Materials

Halide perovskites have emerged as promising materials for a wide variety of optoelectronic applications, including solar cells, light‐emitting devices, photodetectors, and quantum information applications. In addition to their desirable optical and electronic properties, halide perovskites provide tremendous synthetic flexibility through variation of not only their chemical composition but also their structure and morphology. At the heart of their use in optoelectronic technologies is the interaction of light with electronic excitations in the form of excitons. This review discusses the properties and behavior of excitons in halide perovskite materials, with a particular emphasis on low‐dimensional perovskites and the effects of nanoscale morphology on excitonic behavior. The basic theory of excitonic energy migration in semiconductor nanomaterials is introduced, and novel observations in halide perovskite nanomaterials that have evolved our current understanding are explored. Lastly, many important questions that remain unanswered are presented and exciting emerging directions in low‐dimensional perovskite exciton physics are discussed.

2D materials↗
DOE OSTI2021

Outlier classification using autoencoders: application for fluctuation driven flows in fusion plasmas

Understanding the statistics of fluctuation driven flows in the boundary layer of magnetically confined plasmas is desired to accurately model the lifetime of the vacuum vessel components. Mirror Langmuir probes (MLPs) are a novel diagnostic that uniquely allow us to sample the plasma parameters on a time scale shorter than the characteristic time scale of their fluctuations. Sudden large-amplitude fluctuations in the plasma degrade the precision and accuracy of the plasma parameters reported by MLPs for cases in which the probe bias range is of insufficient amplitude. While some data samples can readily be classified as valid and invalid, we find that such a classification may be ambiguous for up to 40% of data sampled for the plasma parameters and bias voltages considered in this study. In this contribution, we employ an autoencoder (AE) to learn a low-dimensional representation of valid data samples. By definition, the coordinates in this space are the features that mostly characterize valid data. Ambiguous data samples are classified in this space using standard classifiers for vectorial data. In this way, we avoid defining complicated threshold rules to identify outliers, which require strong assumptions and introduce biases in the analysis. By removing the outliers that are identified in the latent low-dimensional space of the AE, we find that the average conductive and convective radial heat fluxes are between approximately 5% and 15% lower as when removing outliers identified by threshold values. For contributions to the radial heat flux due to triple correlations, the difference is up to 40%.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Aerodynamic Sensitivities over Separable Shape Tensors

Here, we present a comprehensive aerodynamic sensitivity analysis of airfoil parameterization informed by separable shape tensors. This parameterization approach uniquely benefits the design process by isolating various well-studied shape characteristics, such as airfoil thickness, and providing a well-regulated low-dimensional parameter domain for aerodynamic designs. Exploring the aerodynamic sensitivities of this novel parameterization can provide valuable insights for more robust designs and future manufacturing efforts. We construct a data-driven parameter space of airfoils using principal geodesic analysis of separable shape tensors informed by a curated database containing almost 20,000 suitable engineering airfoils. Analyzing the shape reconstruction error and the maximum mean discrepancy between joint distributions of aerodynamic quantities, we study the dimensionality of the learned parameter space. This simple numerical experiment demonstrates a dramatic dimension reduction that retains design effectiveness and promotes regularity of the shape representations. Finally, we generate new airfoils and use the HAM2D Reynolds-averaged Navier–Stokes solver to predict lift, drag, and moment coefficients. We compute multiple sensitivity metrics to quantify and assert the consistency of parameter influence on the aerodynamic quantities. We also explore low-dimensional polynomial ridge approximations to motivate physical intuitions and offer explanations of the approximated sensitivities.

17 WIND ENERGY↗
DOE OSTI2021

Ultrabroadband Nanocavity of Hyperbolic Phonon–Polaritons in 1D-Like α-MoO 3

The exploitation of phonon–polaritons in nanostructured materials offers a pathway to manipulate infrared (IR) light for nanophotonic applications. Notably, hyperbolic phonon–polaritons (HP 2 ) in polar bidimensional crystals have been used to demonstrate strong electromagnetic field confinement, ultraslow group velocities, and long lifetimes (up to ~12 ps). Here we present nanobelts of α-phase molybdenum trioxide (α-MoO 3 ) as a low-dimensional medium supporting HP 2 modes in the mid- and far-IR ranges. Through real-space nanoimaging techniques with synchrotron and tunable laser IR light, we observe HP 2 Fabry-Perot resonances that demonstrate distinct anisotropic propagation and frequency dependence. We remark an anisotropic propagation that critically depends on the frequency range. Our findings are supported by the convergence of experiment, theory, and numerical simulations. Our work shows that the low dimensionality of natural nanostructured crystals, like α-MoO 3 nanobelts, provides an attractive platform to study polaritonic light–matter interactions and offers appealing cavity properties that could be harnessed in future designs of compact nanophotonic devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

In this work, we present Lift & Learn, a physics-informed method for learning low-dimensional models for large-scale dynamical systems. The method exploits knowledge of a system’s governing equations to identify a coordinate transformation in which the system dynamics have quadratic structure. This transformation is called a lifting map because it often adds auxiliary variables to the system state. The lifting map is applied to data obtained by evaluating a model for the original nonlinear system. This lifted data is projected onto its leading principal components, and low-dimensional linear and quadratic matrix operators are fit to the lifted reduced data using a least-squares operator inference procedure. Analysis of our method shows that the Lift & Learn models are able to capture the system physics in the lifted coordinates at least as accurately as traditional intrusive model reduction approaches. This preservation of system physics makes the Lift & Learn models robust to changes in inputs. Numerical experiments on the FitzHugh–Nagumo neuron activation model and the compressible Euler equations demonstrate the generalizability of our model.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

TPSAS-NF1676L-35020-DND

Low dimensionality materials continue to gain interest in the research community due to a combination of unique optical, electronic transport, and structural properties. Carbon Nanotubes and graphene are two such materials that are attractive for structural health monitoring applications. Single layer graphene is transparent, conductive, strong, flexible, and amenable to deposition by inkjet printing techniques. Carbon nanotubes are extremely strong and lightweight, and available in forms that can be printed, stitched or applied as a sensory patch on a structure. In this work the flexibility, conductivity, and processing capabilities of these low dimensionality carbon structures are coupled to produce directly printed strain sensors. Experimental results from coupon testing through full-scale flight tests are presented and a path toward structural systems with inherent health monitoring discussed.

Buzz Wincheski↗